Video Copy Detection on the Internet: The Challenges of Copyright and Multiplicity

This paper presents applications for dealing with videos on the web, using an efficient technique for video copy detection in large archives. Managing videos on the web is the source of two exciting challenges: the respect of the copyright and the linkage of multiple videos. We present a technique called ViCopT for Video Copy Tracking which is based on labels of behavior of local descriptors computed along video. The re-sults obtained on large amount of data (270 hours of videos from the Internet) are very promising, even with a large video database (700 hours): ViCopT displays excellent robustness to various severe signal transformations, making it able to identify copies accurately from highly similar videos, as well as to link similar videos, in order to reduce redundancy or to gather the metadata associated. Finally, we also show that ViCopT goes further by detecting segments having the same background, with the aim of linking videos of the same cate-gory, like forecast weather programs or particular TV shows. 1

New Insights into the Phylogeny and Molecular Classification of Nicotinamide Mononucleotide Deamidases

Nicotinamide mononucleotide (NMN) deamidase is one of the key enzymes of the bacterial pyridine nucleotide cycle (PNC). It catalyzes the conversion of NMN to nicotinic acid mononucleotide, which is later converted to NAD+ by entering the Preiss-Handler pathway. However, very few biochemical data are available regarding this enzyme. This paper represents the first complete molecular characterization of a novel NMN deamidase from the halotolerant and alkaliphilic bacterium Oceanobacillus iheyensis (OiPncC). The enzyme was active over a broad pH range, with an optimum at pH 7.4, whilst maintaining 90 % activity at pH 10.0. Surprisingly, the enzyme was quite stable at such basic pH, maintaining 61 % activity after 21 days. As regard temperature, it had an optimum at 65 °C but its stability was better below 50 °C. OiPncC was a Michaelian enzyme towards its only substrate NMN, with a Km value of 0.18 mM and a kcat/Km of 2.1 mM-1 s-1. To further our understanding of these enzymes, a complete phylogenetic and structural analysis was carried out taking into account the two Pfam domains usually associated with them (MocF and CinA). This analysis sheds light on the evolution of NMN deamidases, and enables the classification of NMN deamidases into 12 different subgroups, pointing to a novel domain architecture never before described. Using a Logo representation, conserved blocks were determined, providing new insights on the crucial residues involved in the binding and catalysis of both CinA and MocF domains. The analysis of these conserved blocks within new protein sequences could permit the more efficient data curation of incoming NMN deamidases

Large Scale Disk-Based Metric Indexing Structure for Approximate Information Retrieval by Content

In order to achieve large scalability, indexing structures are usually distributed to incorporate more of expensive main memory during the query processing. In this paper, an indexing structure, that does not suffer from a performance degradation by its transition from main memory storage to hard drive, is proposed. The high efficiency of the index is achieved using a very effective pruning based on precomputed distances and so called locality phenomenon which substantially diminishes the number of retrieved candidates. The trade-offs for the large scalability are, firstly, the approximation and, secondly, longer query times, yet both are still bearable enough for recent multimedia content-based search systems, proved by an evaluation using visual and audio data and both metric and semi-metric distance functions. The tuning of the index’s parameters based on the analysis of the particular’s data intrinsic dimensionality is also discussed.

Object detection and localization using a knowledge graph on spatial relationships

International audienceA knowledge on spatial relationships between objects present in a given collection of images can provide interesting information to improve classical CBIR tasks such as object detection and localization, by reducing the searching areas of the object relatively to one or several given objects. In this paper, we propose a representation of the knowledge on relationships existing between symbolic objects in a collection of images. None exhaustively, these relationships can be co-occurrences of objects or different kinds of spatial relationships between them in images. We present a graph-based representation of this knowledge and its associated operations and properties. This work was evaluated on the public symbolic image database LabelMe. The experiments show its relevance for object detection and localization

Automatic Textual Annotation Of Video News Based on Semantic Visual Object Extraction

In this paper, we present our work for automatic generation of textual metadata based on visual content analysis of video news. We present two methods for semantic object detection and recognition from a cross modal image-text thesaurus. These thesaurus represent a supervised association between models and semantic labels. This paper is concerned with two semantic objects: faces and Tv logos. In the first part, we present our work for efficient face detection and recogniton with automatic name generation. This method allows us also to suggest the textual annotation of shots close-up estimation. On the other hand, we were interested to automatically detect and recognize different Tv logos present on incoming different news from different Tv Channels. This work was done jointly with the French Tv Channel TF1 within the "MediaWorks " project that consists on an hybrid text-image indexing and retrieval plateform for video news

Qualitative Comparison of Audio and Visual Descriptors Distributions

Abstract—A comparative study of distributions and properties of datasets representing public domain audio and visual content is presented. The criteria adopted in this study incorporate the analysis of the pairwise distance distribution histograms and estimation of intrinsic dimensionality. In order to better understand the results, auxiliary datasets have been also considered and analyzed. The results of this study provide a solid ground for further research using the presented datasets such as their indexability with index structures. I